I'd like to retrain a YOLOv7-tiny Object Detector on a custom dataset with 4 classes. According to the README of the official git repo in order to transfer learn one should pass the pre-trained weights as argument:
python train.py --workers 8 --device 0 --batch-size 32 --data data/custom.yaml --img 640 640 --cfg cfg/training/yolov7-custom.yaml --weights 'yolov7_training.pt' --name yolov7-custom --hyp data/hyp.scratch.custom.yaml
As for the normal YOLOv7 model, there are the weight files yolov7.pt and yolov7_training.pt present in the repo (see here). What´s the difference between these two and which should be used in which occasion?
Also there's a yolov7-tiny.pt weight file, but no respective yolov7-tiny_training.pt file. Is there a particular reason for that?
Also I learned that for Transfer Learning it's helpful to "freeze" the base models weights (make them untrainable) first, then train the new model on the new dataset, so only the new weights get adjusted. After that you can "unthaw" the frozen weights to fine-tune the entire model. The train.py script has a --freeze argument to freeze backbone layers.
Is this approach recommended for retraining YOLOv7? If so, should you freeze all 50 backbone layers of YOLOv7 (and would that command be --freeze 50 or smth. different)? And how would you then unthaw these layers and resume the training of said model?
